1
Department of Veterinary Integrative Biosciences, Texas A&M
University College of Veterinary Medicine & Biomedical Sciences, College
Station, TX 77845-4458, USA2
School of Biological, Earth and Environmental Sciences, University of
New South Wales, Sydney, NSW 2052, Australia

(Received 13 February 2007; accepted 22 October 2007; published online 29 January 2008)

Abstract - Modeling potential disease spread in wildlife populations is important for
predicting, responding to and recovering from a foreign animal disease
incursion. To make spatial epidemic predictions, the target animal species
of interest must first be represented in space. We conducted a series of
simulation experiments to determine how estimates of the spatial
distribution of white-tailed deer impact the predicted magnitude and
distribution of foot-and-mouth disease (FMD) outbreaks. Outbreaks were
simulated using a susceptible-infected-recovered geographic automata model.
The study region was a 9-county area (24 000 km2) of southern Texas.
Methods used for creating deer distributions included dasymetric mapping,
kriging and remotely sensed image analysis. The magnitudes and distributions
of the predicted outbreaks were evaluated by comparing the median number of
deer infected and median area affected (km2), respectively. The methods
were further evaluated for similar predictive power by comparing the model
predicted outputs with unweighted pair group method with arithmetic mean
(UPGMA) clustering. There were significant differences in the estimated
number of deer in the study region, based on the geostatistical estimation
procedure used (range: 385 939-768 493). There were also substantial
differences in the predicted magnitude of the FMD outbreaks (range:
1 563-8 896) and land area affected (range: 56-447 km2) for the
different estimated animal distributions. UPGMA clustering indicated there
were two main groups of distributions, and one outlier. We recommend that
one distribution from each of these two groups be used to model the range of
possible outbreaks. Methods included in cluster 1 (such as county-level
disaggregation) could be used in conjunction with any of the methods in
cluster 2, which included kriging, NDVI split by ecoregion, or
disaggregation at the regional level, to represent the variability in the
model predicted outbreak distributions. How animal populations are
represented needs to be considered in all spatial disease spread models.